A Monte Carlo Approach to

Estimating Impacts from

Highly Intermittent Sources on

Short Term Standards

Clint Bowman and Ranil Dhammapala, State of Washington, Department of Ecology

Estimating Impacts from Highly Intermittent Sources on Short Term Standards

Problem Description Modeling Approaches Support for Statistical Approach Recipe Compute Requirements

Problem Description

Multiple megawatt generators at each data center

Multiple data centers in small communities Wenatchee Moses Lake Quincy

Annual Diesel PM

Problem Description (2)

Standard defined over several years Standard defined as percentile (98th) Sources are highly intermittent (1 – 2 % duty

cycle) Ground level impact dependent on meteorology Source operation not correlated with dispersion

conditions

Modeling ApproachesDeterministic

Screen—expected emission rate for each mode Pass if highest impact is below NAAQS Pass if 8th high of each year is less than NAAQS Pass if running 3-year average of 8th high < NAAQS

Refined(1)—specify day of week and times Lowers probability that high emissions mode lands on

poor dispersion day But meteorology doesn’t understand day of week

Refined(2)—step through days of week Still misses many possible combinations of emissions

and meteorology

Rely on Recent Experience

Chronology

Investigated effects of sampling frequency on computed 98th percentile (1:1 to 1:6 day rates)

Applied Monte Carlo to sample observed daily concentrations

Applied same Monte Carlo method to model output with similar results

Monte Carlo method seemed appropriate to apply to evaluate impacts of intermittent sources on a statistically-based metric

Support for Statistical Approach

Numerical experiments Previous application to problems in:

Physical sciences Engineering Biology Applied statistics Finance Telecommunications

Statistical Experiments

Generate a log-normally distributed dataset of 1825 observations corresponding to five years of daily observations

Define operating modes (emission rates and number of days per year)

Sample the distribution (without replacement) according to the defined modes, compute 98th percentile, and repeat

Determine effect on computed 98th percentile of varying number of samples drawn

Mode Definitions

Mode % Power Days/YearWeekly 51 12Monthly 7.6 12Semi-A 23 2Annual 23 2Outage 100 8

Modeling Requirements

Define all distinct modes of operation Power levels Duty cycle

Run AERMOD for each mode Save hourly output in POST file

Define daily maxima at each receptor for each day of run

Example of Run times

AERMOD required 75 hours for 15 modes Perl script processing *.POST files – 35 hours R script for samples 65 hours

There has been a 2 – 5 times speedup since this benchmark was run

Recipe

Define Modes Run Dispersion Model Retrieve Daily Maxima Randomly Select Days Compute 98th Percentile Repeat 1000 Times Compute Median